doi:10.1016/j.eswa.2004.08.003 DTD 5 ARTICLE IN PRESS Expert system methodologies and applications—a decade review from 1995 to 2004 Shu-Hsien Liao* Department of Management Sciences and Decision Making, Tamkang University, No. 151, Yingjuan Rd, Danshuei Jen, Taipei 251, Taiwan, ROC Abstract This paper surveys expert systems (ES) development using a literature review and classification of articles from 1995 to 2004 with a keyword index and article abstract in order to explore how ES methodologies and applications have developed during this period. Based on the scope of 166 articles from 78 academic journals (retrieved from five online database) of ES applications, this paper surveys and classifies ES methodologies using the following eleven categories: rule-based systems, knowledge-based systems, neural networks, fuzzy ESs, object- oriented methodology, case-based reasoning, system architecture, intelligent agent systems, database methodology, modeling, and ontology together with their applications for different research and problem domains. Discussion is presented, indicating the followings future development directions for ES methodologies and applications: (1) ES methodologies are tending to develop towards expertise orientation and ES applications development is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science, and human behavior could implement ES as another kind of methodology. (3) The ability to continually change and obtain new understanding is the driving power of ES methodologies, and should be the ES application of future works. q 2004 Published by Elsevier Ltd. Keywords: Expert systems; Expert system methodologies; Expert system applications; Literature survey 1. Introduction Expert systems (ES) are a branch of applied artificial intelligence (AI), and were developed by the AI community in the mid-1960s. The basic idea behind ES is simply that expertise, which is the vast body of task-specific knowledge, is transferred from a human to a computer. This knowledge is then stored in the computer and users call upon the computer for specific advice as needed. The computer can make inferences and arrive at a specific conclusion. Then like a human consultant, it gives advices and explains, if necessary, the logic behind the advice (Turban & Aronson, 2001). ES provide powerful and flexible means for obtaining solutions to a variety of problems that often cannot be dealt with by other, more traditional and orthodox methods. Thus, their use is proliferating to many sectors of our social and technological life, where their applications 0957-4174/$ - see front matter q 2004 Published by Elsevier Ltd. doi:10.1016/j.eswa.2004.08.003 * Tel.: C886-2947-2044; fax: C886-2945-3007. E-mail address: michael@mail.tku.edu.tw. are proving to be critical in the process of decision support and problem solving. As a part of ES research, this paper surveys the development of ES through a literature review and classification of articles from 1995 to 2004 as a basis, exploring the ES methodologies and applications during that period. The reason for choosing this period is that the Internet was opened to general users in 1994 and this new era of information and communication technology has played important roles, not only in the field of ES, but also in the ability to collect data from online database. This literature survey started on March 2003 and it was based on a search in the keyword index and article abstract for ‘ES’ on the Elsevier SDOS, IEEE Xplore, EBSCO (electronic journal service), Ingenta, and Wiley InterScience online database, for the period from 1995 to 2004, in which 10,439 articles were updated and found on June 2004. After topic filtering, there were 166 articles from 78 journals related to the keyword ‘ES applications’, 98 of which were connected to the methodology of keyword ‘ES methodology’. Based on the scope of 166 articles on ES applications, this paper surveys and classifies ES methodologies using eleven Expert Systems with Applications xx (2004) 1–11 www.elsevier.com/locate/eswa http://www.elsevier.com/locate/eswa Table 1 Rule-based systems and their applications Rule-based systems/applications Authors State transition analysis Ilgun, Kemmerer, and Porras (1995) Psychiatric treatment Goethe and Bronzino (1995) Production planning Hamada, Baba, Sato, and Yufu (1995) Advisory system Kose et al. (1995) Teaching Chan, Ma, Chan, and Chen (1995) Electronic power planning Rahman and Hazim (1996) Automobile process planning Sabourin and Villeneuve (1996) Hypergraph representation Ramaswamy, Sarkar, and Chen (1997) System development Mulvaney and Bristow (1997) Knowledge verification/vali- Wu and Lee (1997) Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–112 DTD 5 ARTICLE IN PRESS categories: rule-based systems, knowledge-based systems, neural networks, fuzzy ESs, object-oriented methodology, case-based reasoning (CBR), system architecture develop- ment, intelligent agent (IA) systems, modeling, ontology, and database methodology together with their applications for different research and problem domains. The rest of the paper is organized as follows. Sections 2–12 present the survey results of ES methodologies and applications based on the above categories, respectively. Section 13 presents some discussion, extending to sugges- tions for future development of ES methodologies and applications. Finally, Section 14 contains a brief conclusion. dation Alcohol production Guerreiro et al. (1997) DNA histogram interpretation Marchevsky, Truong, and Tolmachoff (1997) Knowledge base maintenance Higa and Lee (1998) Scheduling strategy Zupan and Cheng (1998) Management fraud assessment Deshmukh and Talluru (1998) Knowledge acquisition Wu and Chen (1999) Communication system fault diagnosis Leon, Mejias, Luque, and Gonzalo (1999) Bioseparation Lienqueo, Salgado, and Asenjo (1999) Material processing design Kim and Im (1999) Resource utilization McCoy and Levary (2000) Biochemical nanotechnology Wasiewicz, Janczak, Mulawka, and Plucienniczak (2000) Probabilistic fault diagnosis Leung and Romagnoli (2000) Agriculture planning Plant and Vayssieres (2000) Load scheduling Croce et al. (2001) Apiculture Mahaman et al. (2002) Agricultural diagnostic/advisory Mahaman, Passam, Sideridis, and Yialouris (2003) Geoscience Soh, Tsatsoulis, Gineris, and Bertoia (2004) Sensor control Valenzuela, Bentley, and Lorenz (2004) Tutoring system Hatzilygeroudis and Prentzas (2004) Knowledge representation Hatzilygeroudis and Prentzas (2004) 2. Rule-based systems and their applications A rule-based ES is defined as one, which contains information obtained from a human expert, and represents that information in the form of rules, such as IF–THEN. The rule can then be used to perform operations on data to inference in order to reach appropriate conclusion. These inferences are essentially a computer program that provides a methodology for reasoning about information in the rule base or knowledge base, and for formulating conclusions. Applications of rule-based systems on ESs are including: state transition analysis, psychiatric treatment, production planning, advisory system, teaching, electronic power planning, automobile process planning, hypergraph rep- resentation, system development, knowledge verification/ validation, alcohol production, DNA histogram interpre- tation, knowledge base maintenance, scheduling strategy, management fraud assessment, knowledge acquisition, knowledge representation, communication system fault diagnosis, bioseparation, material processing design, resource utilization, biochemical nanotechnology, proba- bilistic fault diagnosis, agriculture planning, load schedul- ing, apiculture, tutoring system, geoscience, and sensor control. The methodology of rule-based systems and their applications are categorized in Table 1. 3. Knowledge-based systems and their applications The most common definition of KBS is human-centered. This highlights the fact that KBS have their roots in the field of artificial intelligence (AI) and that they are attempts to understand and initiate human knowledge in computer systems (Wiig, 1994). The four main components of KBS are usually distinguished as: a knowledge base, an inference engine, a knowledge engineering tool, and a specific user interface (Dhaliwal & Benbasat, 1996). On the other hand, the term KBS includes all the organizational information technology applications that may prove helpful to manage the knowledge assets of an organization, such as ESs, rule- based systems, groupware, and database management systems (DBMS) (Laudon & Laudon, 2002). Some of these applications which are implemented by knowledge-based systems include the following: medical treatment, personal finance planning, engineering failure analysis, waste management, production management, thermal engineering, decision support, knowledge manage- ment, knowledge representation, power electronics design, framed buildings evaluation, financial analysis, chemical incident management, automatic tumor segmentation, business game, climate forecasting, agricultural manage- ment, steel composition design, strategic management, environmental protection, wastewater treatment, decision making and learning, isokinetics interpretation, chemical process controlling, therapy planning, plant process control, outage locating planning, concurrent system design, case validation, chip design, agriculture planning, power trans- mission protection, crop production planning, tropospheric chemistry modeling, planar robots, and urban design. The methodology of knowledge-based systems and their appli- cations are categorized in Table 2. Table 2 Knowledge-based systems and their applications Knowledge-based systems/ applications Authors Medical treatment Alonso-Amo, Perez, Gomez, and Montes (1995) Personal finance planning Dirks, Kingston, and Haggith (1995) Engineering failure analysis Graham-Jones and Mwllor (1995) Waste management Wei and Weber (1996) Production management Dawood (1996) Thermal engineering Afgan and Carvalho (1996) Decision support Keefe and Preece (1996) Knowledge management Dutta (1997) Knowledge representation Mitra and Basu (1997) Framed buildings evaluation Lu and Simmonds (1997) Power electronics design Fezzani, Piquet, and Foch (1997) Financial analysis Matsatsinis, Doumpos, and Zopounidis (1997) Chemical incident management Finch and Lees (1997) Automatic tumor segmentation Clark et al. (1998) Business game Duan, Edwards, and Robins (1998) Climate forecasting Rodionov and Martin (1999) Agricultural management Girard and Hubert (1999) Steel composition design Manohar, Shivathaya, and Ferry (1999) Strategic management Volberda and Rutges (1999) Environmental protection Gomolka and Orlowski (2000) Wastewater treatment Baeza, Ferreira, and Laufuente (2000) Decision making and learning Mockler, Dologite, and Gartenfeld (2000) Isokinetics interpretation Alonso, Fuertes, Martinez, and Montes (2000) Chemical process controlling Barrera-Cortes, Astruc, and Tufeu (2001) Physical therapy planning Tunez, Aguila, and Marin (2001) Plant process control Acosta, Gonzalez, and Pulido (2001) Outage locating planning Liu and Schulz (2002) Concurrent system design Mills and Gomaa (2002) Case validation Knauf, Gonzalez, and Abel (2002) Chip design Bourbakis, Mogzadeh, Mertoguno, and Koutsougeras (2002) Agricultural planning Cohen and Shoshany (2002) Power transmission protection Orduna, Garces, and Handschin (2003) Crop production planning Edrees, Rafea, Fathy, and Yahia (2003) Tropospheric chemistry model- ing Saunders, Pascoe, Johnson, Pilling, and Jenkin (2003) Urban design Xirogiannis, Stefanou, and Glykas (2004) Planar robots Sen, Minambres, Garrido, Almansa, and Soto (2004) Table 3 Neural networks and their applications Neural networks/applications Authors Fault diagnosis Wang, Qu, Liu, and Cheng (2004), Yang, Han, and Kim, (2004) Optimal power flow Decision making Alarm processing system Inference mechanisms Diagnostic system Machine learning Fu (1998) Power load forecasting Facility layout design Process control Knowledge learning Gold mining process design Robotic systems Parameter setting Waste treatment Biomedical application Mitigation processes control Engineering ceramics Acoustic signal diagnosing Li, Tasi, Tasi, and Chiu (2004) Crude oil distillation Liau et al. (2004) Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–11 3 DTD 5 ARTICLE IN PRESS 4. Neural networks and their applications An artificial neural network (ANN) is a model that emulates a biological neural network. This concept is used to implement software simulations for the massively parallel processes that involve processing elements inter- connected in network architecture. The artificial neuron receives inputs that are analogous to the electrochemical impulses that the dendrites of biological neurons receive from other neurons. The output of the artificial neuron corresponds to signals sent out from a biological neuron over its axon. These artificial signals can be changed similarly to the physical changes occurring at neural synapses (Turban & Aronson, 2001). Some of the applications that are implemented by neural networks are the following: fault diagnosis, optimal power flow, decision making, alarm processing system, inference mechanisms, diagnostic system, machine learning, power load forecasting, facility layout design, process control, knowledge learning, gold mining process design, robotic systems, parameter setting, waste treatment, engineering ceramics, mitigation processes control, acoustic signal diagnosing, crude oil distillation, and biomedical appli- cation. The methodology of neural networks and their applications are categorized in Table 3. 5. Fuzzy expert systems and their applications Fuzzy ESs are developed using the method of fuzzy logic, which deals with uncertainty. This technique, which uses the mathematical theory of fuzzy sets, simulates the process of normal human reasoning by allowing the computer to behave less precisely and logically than conventional computers. This approach is used because decision-making is not always a matter of black and white, true or false; it often involves gray areas and the term may be. Accordingly, creative decision-making processes can be characterized unstructured, playful, contentious, and ram- bling (Jamshidi, Titli, Zadeh, & Boverie 1997). Some applications implemented by fuzzy ESs are such as: power load forecasting, online scheduling, chemical Table 4 Fuzzy expert systems and their applications Fuzzy expert systems/ applications Authors Power load forecasting Kim, Park, Hwang, and Kim (1995) Online scheduling Chang and Thia (1996) Chemical process fault diagnosis Ozyurt and Kandel (1996) Ecological planning Zhu, Band, Dutton, and Nimlos (1996) Power system diagnosis Cho and Park (1997) Control systems Bugarin and Barro (1998) Uncertainly reasoning Pan, DeSouza, and Kak (1998) Knowledge integration Lee, Han, Song, and Lee (1998) Fault diagnosis Lee et al. (2000) and Soliman, Rizzoni, and Kim (1999) Power system classification Dash, Mishra, Salama, and Liew (2000) Fault detection EI-Shal and Morris (2000) Demand evaluation Benson and Asgarpoor (2000) Wastewater treatment Carrasco, Rodriguez, Punal, Roca, and Lema (2004), Punal, Rodriguez, Car- rasco, Roca, and Lema (2002), and Punal et al. (2001) Machinability data selection Wong and Hamouda (2002, 2003) Water supply forecast Mahabir, Hicks, and Fayek (2003) Radiography classification Liao (2003) On-line analytic processing Leung, Lau, and Kwong (2003) Hotel selection Ngai and Wat (2003) Dryer tool integration Lababidi and Baker (2003) Medical diagnosis Meesad and Yen (2003) and Sendelj and Devedzic (2004) Pooled flood frequency analysis Shu and Burn (2004) Medical consultation system Boegl, Adlassnig, Hayashi, Rothenfluh, and Leitich (2004) Job matching Drigs et al. (2004) Performance indexing Padilla-Medina and Sanchez-Marin (2004) Computer security Reznik and Dabke (2004) Gesture recognition Frantti and Kallio (2004) Table 5 Object-oriented methodology and their applications Object-oriented methodology/ applications Authors Industry diagnosis Batanov and Cheng (1995) Knowledge representation Vranes and Stanojevic (1995) Electronic power capacity planning Deb (1995) Knowledge learning Menzies (1997) Power system maintenance Kawahara, Sasaki, Kubokawa, Asahara, and Sugiyama (1998) Knowledge engineering Geymayr and Ebecken (1998) Manufacturing information network Lau, Tso, and Ho (1998) Syntactic programming Depradine (2003) Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–114 DTD 5 ARTICLE IN PRESS process fault diagnosis, ecological planning, control sys- tems, uncertainly reasoning, knowledge integration, fault diagnosis, power system classification, fault detection, demand evaluation, wastewater treatment, machinability data selection, water supply forecast, radiography classifi- cation, on-line analytic processing, hotel selection, dryer tool integration, pooled flood frequency analysis, medical consultation system, job matching, performance indexing, computer security, gesture recognition, and medical diag- nosis. The methodology of fuzzy ESs together with their applications is categorized in Table 4. 6. Object-oriented methodology and their applications Object-oriented methodology combines into one object data together with the specific procedures that operate on this data, where the object combines data and program code. Instead of passing data to procedures, programs send a message for an object to perform a procedure that is already embedded in it. Then, the same message may be sent to many different objects, but each will implement that message differently. An object’s data are encapsulated from other parts of the system, so each object is an independent software building block that can be used in many different systems without changing the program code. Some applications implemented by object-oriented methodology include the following: industry diagnosis, knowledge learning, manufacturing information network, power system maintenance, knowledge engineering, syn- tactic programming, and knowledge representation. The methodology of object-oriented methodology and their applications are categorized in Table 5. 7. Case-based reasoning and their applications The basic idea of CBR is to adapt solutions that were used to solve previous problems and use them to solve new problems. In CBR, descriptions of past experience of human specialists, represented as cases, are stored in a database for later retrieval when the user encounters a new case with similar parameters. The system searches for stored cases with problem characteristics similar to the new one, finds the closest fit, and applies the solutions of the old case to the new case. Successful solutions are tagged to the new case and both are stored together with the other cases in the knowledge base. Unsuccessful solutions also are appended to the case base along with explanations as to why the solutions did not work (Kolonder, 1994). Some of the applications implemented by CBR include the following: manufacturing process design, knowledge management, power system restoration training, ultrasonic inspection, medical planning, medical application, fault diagnosis, e-learning, and knowledge modeling. These CBR and their applications are categorized in Table 6. 8. Modeling and their applications Modeling methodology becomes an interdisciplinary methodology of ES in order to build formal relationships with logical model design in different knowledge/problem Table 6 Case-based reasoning and their applications Case-based reasoning/applications Authors Manufacturing process design Takahashi, Oono, and Saitog (1995) Knowledge management Noh, Lee, Kim, Lee, and Kim (2000) Power system restoration training Islam and Chowdhury (2001) Ultrasonic inspection Jarmulak, Kerckhoffs, and Veen (2001) Medical planning Abidi and Manickam (2002) Medical application Montani and Bellazzi (2002) Knowledge modeling Gardan and Gardan (2003) Fault diagnosis Yang et al. (2004) E-learning Fu and Shen (2004) Table 8 System architecture and their applications System architecture/applications Authors Material evaluation and selection Mahmoud and AL-Hammad (1996) Computer aided design Tucho, Sierra, Fernandez, Vijande, and Moris (2003) and Vranes and Stanojevic (1999) Ergonomics design Gilad and Karni (1999) ISO system implementation Khan and Hafiz (1999) Corporate recovery decision sup- port Collier, Leech, and Clark (1999) Concurrent engineering Reidsema and Szczerbicki (2001) Military application Liao (2001) Training simulator Lopez, Flores, and Garcia (2003) Ferryboat configuration Shaalan, Rizk, Abdelhamid, and Bahgat (2004) Liquid retaining structure design Chau and Albermani (2004) Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–11 5 DTD 5 ARTICLE IN PRESS domains. Furthermore, modeling technology can provide quantitative methods to analyze data to represent or acquire expert knowledge with inductive logic programming or algorithms so that AI, cognitive science and other research fields could have broader platforms to implement techno- logies for ES development. The applications implemented by modeling are such as: process control, medical analysis, management decision- making, software evaluation, medical system validation, assembly task planning and simulation, transport terminal design, project allocation, and endometrial hyperplasia classification. The methodology of modeling and their applications are categorized in Table 7. 9. System architecture and their applications System architecture of an ES is similar to an architectural sketch of a house. It gives users a general idea of what the system will look like and how it is going to implement systems. The architecture shows the general capabilities of the system, the users’ interfaces, system functions, system (data) flow, system management, DBMS, necessary proto- col, and specific programming language, such as blackboard architecture, CommonKADS, etc. Once the system Table 7 Modeling and their applications Modeling/applications Authors Process control Medical analysis Peng, Xiao, Nie, Wang, and Wang (1996) Management decision-making Mookerjee and Mannino (1997) Software evaluation Vlahavas, Stamelos, Refanidis, and Tsoukias (1999) Medical system validation Martin-Baramera et al. (2000) Assembly task planning and simulation Zha and Lim (2000) Endometrial hyperplasia classi- fication Morrison et al. (2002) Transport terminal design Abacoumkin and Ballis (2004) Project allocation Cheung, Hong, and Ang (2004) architecture design and implementation are completed, users can manipulate and control system functions on the system architecture. Some of the applications implemented by system architecture illustrate the following: material evaluation and selection, computer aided design, ergonomics design, ISO system implementation, corporate recovery decision support, concurrent engineering, military application, train- ing simulator, liquid retaining structure design, and ferry- boat configuration. These System architectures and their applications are categorized in Table 8. 10. Intelligent agents and their applications An IA is a computer program that helps a user with routine computer tasks. This is a new technology, and as such there are several definitions, database capabilities, and different applications in autonomous programs. Several of the names used to describe IAs are include software agents, wizards, and multi-agent (Turban & Aronson, 2001). Some of the applications implemented by IAs are such as: tutoring systems, system analysis and design, electronic service maintenance, carbon contamination rules, know- ledge representation, adaptive systems, air pollution control, building architecture design, agricultural decision support, industry simulation, and knowledge engineering on the WWW platform. The methodology of IAs together with their applications is categorized in Table 9. 11. Ontology and their applications Ontology is a system of vocabulary, which is used as a fundamental concept for describing the task/domain know- ledge to be identified. This vocabulary is used as a communication basis between domain experts and know- ledge engineers. Accordingly, a reusable task/domain model can be represented and a computer program code is Table 9 Intelligent agents and their applications Intelligent agents/applications Authors Tutoring systems Cruces and Arriaga (2000) Supply chain Gjerdrum, Shah, and Papageorgiou (2001) System analysis and design Gruer, Hilaire, Koukam, and Cet- narowicz (2002) Electronic service maintenance Yu, Iung, and Panetto (2003) Carbon contamination rules Vegh (2003) Knowledge representation Qian, Li, Jiang, and Wen (2003) Industry simulation Aldea et al. (2004) Adaptive systems Juuso (2004) Air pollution control Zhou, Huang, and Chan (2004) Building architecture design Alibaba and Ozdeniz (2004) Olive oil automation Bonstre et al. (2004) Agricultural decision support Thomson and Willoughby (2004) Knowledge engineering on the WWW platform Shaalan, EI-Badry, and Rafea (2004) Table 11 Database methodology and their applications Database methodology/ applications Authors Power system planning Park and Lee (1995) Geography planning Kirkby (1996) Geographical information system Filis, Sabrakos, Yialouris, Sideridis, and Mahaman (2003) Sedimentary rock interpretation Abel, Silva, Ros, Mastella, and Camp- bell (2004) Traditional Chinese medicine diagnosis Wang et al. (2004) Medical expert system Yan et al. (2004) Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–116 DTD 5 ARTICLE IN PRESS generated in that ontology for knowledge acquisition, reuse, heuristic learning. Some applications implemented by ontology include the following: medical decision support, knowledge reuse, preventive control, landscape assessment, knowledge acquisition, and chess heuristic pruning. These ontology and their applications are categorized in Table 10. 12. Database methodology and their applications A database is a collection of data organized to efficiently serve many applications by centralizing the data and minimizing redundant data (McFadden, Hoffer, & Prescott, 2000). A DBMS is the software that permits an organization to centralize data, manage them efficiently, and provide access to the stored data by application programs (Laudon & Laudon, 2002). However, some large databases make knowledge discovery computationally expensive because some domains or background knowledge, hidden in the database may guide and restrict the search for important knowledge. Therefore, modern database methodologies need to process large volumes, multiple hierarchies, and different data formats to discover in-depth expert Table 10 Ontology and their applications Ontology/applications Authors Medical decision support Tu, Eriksson, Gennari, Shahar, and Musen (1995) Knowledge reuse Takaoka and Mizoguchi (1996) Preventive control Thukaram and Parthasarathy (1997) Landscape assessment Martinez-Bejar, Ibanez-Cruz, Compton, and Cao (2001) Knowledge acquisition Ruiz-Sanchez, Valencia-Garcia, Fernandez-- Breis, Martinez-Bejar, and Compton (2003) Chess heuristic pruning Montani and Bellazzi (2002) Knowledge modelling Gardan and Gardan 2(003) knowledge from large databases, such as data mining and searching approach. Some applications implemented by database method- ology present as the following: power system planning, geography planning, geographical information system, sedimentary rock interpretation, traditional Chinese medi- cine diagnosis, and medical ES. The methodology of database methodology and their applications are categor- ized in Table 11. 13. Discussions, limitations, and suggestions 13.1. Discussions ES methodologies and applications are a broad category of research issues on ES Some specific methodologies and methods are presented as examples for exploring the suggestions and solutions to specific ES problem domains. Therefore, methodologies and applications of ES are attracting much attention and efforts, both academic and practical. From this literature review, we can see that ES methodologies and applications developments are diversi- fied due to their authors’ backgrounds, expertise, and problem domains. This is why some authors can appear in the literature on different methodologies and applications. On the other hand, some methodologies have common concepts, and types of methodology. For example, rule- based systems and knowledge-based systems, or fuzzy logic versus ANN methodology. However, a few authors work in different methodologies and applications. This indicates that the trend of development on methodology is also diversified due to author’s research interests and abilities in the methodology and problem domain. This may indicate that the development of ES methodologies is directed toward expertise orientation. Furthermore, some applications have a high degree of overlap in different methodologies. For example, teaching/ training, knowledge acquisition, knowledge representation, knowledge learning, fault diagnosis/detec- tion, medical applications, production planning, system design/development, modeling, process control, decision Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–11 7 DTD 5 ARTICLE IN PRESS making, waste treatment, resource management, biomedical application, robotic systems, forecasting, ecological plan- ning, agriculture planning, geoscience, power system planning, chemical application, industry planning, manage- ment issues, and knowledge reuse, are all topics of different methodologies, which implement ES in a common problem domain. This indicates that those applications are the major trend of ES development, and many methodologies focus on these problems. This may direct development of ES applications toward problem domain orientation. In this paper, most of the articles discussed were from different categories including agriculture, agronomy, auto- mation, biochemistry, biology, chemistry, computer science, biology, ecology, education, energy, engineering, entomology, environmental sciences, genetics, geochemis- try, geology, geosciences, health care sciences, hematology, hydrology, materials, mathematics, mechanics, medical, military, operation research/management sciences, onto- logy, plant science, remote sensing, robotics, and water resources, which retrieved from Elsevier SDOS, IEEE Xplore, EBSCO (electronic journal service), Ingenta, and Wiley InterScience online database. We do not conclude that ES methodologies and applications are not developed in other science fields. However, we would like to see more ES methodologies and applications of different research fields published in order to broaden our horizon of academic and practice works on ES. 13.2. Limitations Firstly, a literature review for the broad category of ES methodologies and applications is a difficult task due to the extensive background knowledge needed for collecting, studying, and classifying these articles. Although limited in background knowledge, this paper makes a brief literature review of ES from 1995 to 2004 in order to explore how ES methodologies and applications have developed in this period. Indeed, the categorization of methodologies and their applications is based on the keyword index and article abstract in this research. Some other articles may have implemented similar ES methodologies in their applications without an ES index, so this paper might not find these reference sources. Therefore, the first limitation of this article is the author’s limited knowledge in presenting an overall picture of this subject. Secondly, although 166 articles from 78 academic journals (five online databases) cited in this paper, there are other academic journals listed in the science citation index (SCI) engineering index (EI), and the social science citation index (SSCI), as well as other academic journals/ magazines, practical articles and reports are not included in this survey. These would have provided more complete information to explore the development of ES methodo- logies and applications. Thirdly, non-English publications are not considered in this survey to determine the effects of different cultures on the development of ES methodologies and applications. We believe that ES methodologies and applications in addition to those discussed in this article have also been developed and published in other areas and languages. 13.3. Suggestions (1) Other social science methodologies. In this article, the definition of ES methodology is not complete because other methodologies, such as social science method- ologies, were not included in the survey. However, qualitative questionnaires and statistical methods are another research technology to solve problems in social studies. For example, cognitive science, psychology and human behavior are used to implement different methods for exploring specific human expert problem. Therefore, other social sciences methodologies may include ES methodology categories in future works. (2) Integration of methodologies. ES is an interdisciplinary research topic. Thus, future ES developments need integration with different methodologies, and this integration of methodologies and cross-interdisciplin- ary research may offer more technologies to investigate ES problems. (3) Change is a source of future ES development. The change due to social and technical reasons may either enable or inhibit ES methodologies and application development. This means that inertia, stemming from the use of routine problem solving procedures, stagnant knowledge sources, and following past experience or knowledge may impede changes in terms of learning and innovation for individuals and organizations. Therefore, to continue creating, sharing, learning, and storing knowledge on different methodologies and application domains may also become a source of ES development. 14. Conclusions This paper is based on a literature review of ES methodologies and applications from 1995 to 2004 using a keyword index and article title search. We conclude that ES methodologies are tending to develop towards expertise orientation and that ES applications development is a problem-oriented domain. It is suggested that different social science methodologies, such as psychology, cognitive science, and human behavior could implement ES as another kind of methodology. Integration of qualitative, quantitative and scientific methods and integration of ES methodologies studies may broaden our horizon on this subject. Finally, the ability to continually change and obtain new understanding is the power of ES methodologies, and will be the ES application of future works. Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–118 DTD 5 ARTICLE IN PRESS References Abacoumkin, C., & Ballis, A. (2004). Development of an expert system for the evaluation of conventional and innovative technologies in the intermodal transport area. European Journal of Operational Research, 152, 410–419. Abel, M., Silva, A. L., Ros, L. F., Mastella, L. S., & Campbell, J. A. (2004). PetroGrapher: managing petrographic data and knowledge using an intelligent database application. Expert Systems with Applications, 26, 9–18. Abidi, S. S. R., & Manickam, S. (2002). Leveraging XML-based electronic medical records to extract experiential clinical knowledge. Inter- national Journal of Medical Informatics, 68, 187–203. Acosta, G., Gonzalez, C. A., & Pulido, B. (2001). Basic tasks for knowledge-based supervision in process control. Engineering Appli- cations of Artificial Intelligence, 14, 441–455. Afgan, N. H., & Carvalho, M. G. (1996). Knowledge-based expert system for fouling assessment of industrial heat exchangers. Applied Thermal Engineering, 16, 203–208. Aldea, A., Banares-Alcantara, R., Jimenez, L., Moreno, A., Martinez, J., & Riano, D. (2004). The scoipe of application of multi-agent systems in the process industry: three case studies. Expert Systems with Applications, 26, 39–47. Alibaba, H. Z., & Ozdeniz, M. B. (2004). A building elements selection system for architects. Building and Environment, 39, 307–316. Alonso, F., Fuertes, J. L., Martinez, L., & Montes, C. (2000). An incremental solution for developing knowledge-based software: its application to an expert system for isokinetics interpretation. Expert Systems with Applications, 18, 165–184. Alonso-Amo, F., Perez, A. G., Gomez, G. L., & Montes, C. (1995). An expert system for homeopathic glaucoma treatment (SEHO). Expert Systems with Applications, 8, 89–99. Baeza, J. A., Ferreira, E. C., & Laufuente, J. (2000). Knowledge-based supervision and control of wastewater treatment plant: a real-time implementation. Water Science and Technology, 41, 129–137. Barrera-Cortes, J., Astruc, J. P., & Tufeu, R. (2001). Knowledge base specification to automate the fluid critical point of fluids. Applied Artificial Intelligence, 15, 453–470. Batanov, D. N., & Cheng, Z. (1995). An object-oriented expert system for fault diagnosis in the ethylene distillation process. Computers in Industry, 27, 237–249. Benson, S. J., & Asgarpoor, S. (2000). A fuzzy expert system for evaluation of demand-side management alternatives. Electronic Machines and Power Systems, 28, 749–760. Boegl, K., Adlassnig, K. P., Hayashi, Y., Rothenfluh, T. E., & Leitich, H. (2004). Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system. Artificial Intelligence in Medicine, 30, 1–26. Bonstre, A., Ors, R., & Peris, M. (2004). Advanced automation of a flow injection analysis system for quality control of olive oil through the use of a distributed expert system. Analytica Chimica Acta, 506, 189–195. Bourbakis, N. G., Mogzadeh, a., Mertoguno, S., & Koutsougeras, C. A. (2002). A knowledge-based expert system for automatic visual VLSI reverse-engineering: VLSI layout version. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 32, 428–437. Bugarin, A. J., & Barro, S. (1998). Reasoning with truth values on compacted fuzzy chained rules. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 28, 34–46. Carrasco, E. F., Rodriguez, J., Punal, A., Roca, E., & Lema, J. M. (2004). Diagnosis of acidification states in an anaerobic wastewater yreatment plant using a fuzzy-based system. Control Engineering Practice, 12, 59–64. Chan, Y. F., Ma, H. K., Chan, H. Y., & Chen, T. Y. (1995). Teaching family planning with expert system. Computers Education, 24, 293–298. Chang, C. S. M., & Thia, B. S. (1996). Online rescheduling of mass rapid transit systems: fuzzy expert system approach. IEE Proceedings Electronic Power Applications, 143, 307–316. Chau, K. W., & Albermani, F. (2004). Hybrid knowledge representation in a blackboard KBS for liquid retaining structure design. Engineering Application of artificial Intelligence, 17, 11–18. Cheung, Y., Hong, G. M., & Ang, K. K. (2004). A dynamic project allocation algorithm for a distributed expert system. Expert Systems with Applications, 26, 225–232. Cho, H. J., & Park, J. K. (1997). An expert system for fault section diagnosis of power systems using fuzzy relations. IEEE Transactions on Power Systems, 12, 342–348. Clark, M. C., Hall, L. O., Goldgof, D. B., Velthuizen, R., Murtagh, R., & Silbiger, M. S. (1998). Automatic tumor segmentation using knowl- edge-based techniques. IEEE Transactions on Medical imaging, 17, 187–201. Cohen, Y., & Shoshany, M. (2002). A national knowledge-based crop recognition in Mediterranean environment. International Journal of Applied Earth Observation, 4, 75–87. Collier, P. A., Leech, S. A., & Clark, N. (1999). A validated expert system for decision making in corporate recovery. International Journal of Intelligent System in Accounting, Finance and Management, 8, 75–88. Croce, F., Delfino, B., Fazzini, P. A., Massucco, S., Morini, A., Silvestro, F., & Sivieri, M. (2001). Operation and management of the electronic system for industrial plants: an expert system prototype for load- scheduling operator assistance. IEEE Transactions on Industry Applications, 37, 701–708. Cruces, A. L. L., & Arriaga, F. D. (2000). Reactive agent design for intelligent tutoring systems. Cybernetics and Systems: An International Journal, 31, 1–47. Dash, P. K., Mishra, S., Salama, M. M., & Liew, A. C. (2000). Classification of power system disturbances using a fuzzy expert system and a fourier linear combiner. IEEE Transactions on power Delivery, 15, 472–477. Dawood, N. N. (1996). A strategy of knowledge elicitation for developing an integrated bidding/production management expert system for the precast industry. Advances in Engineering Software, 25, 225–234. Deb, A. K. (1995). Object-oriented expert system estimated line ampacity. IEEE Computer Application in Power, 12, 30–35. Depradine, C. (2003). Expert system for extracting syntactic information from Java code. Expert Systems with Applications, 25, 187–198. Deshmukh, A., & Talluru, L. (1998). A rule-based fuzzy reasoning system for assessing the risk of management fraud. International Journal of Intelligent Systems in Accounting, Finance and Management, 7, 223–241. Dhaliwal, J. S., & Benbasat, I. (1996). The use and effects of knowledge- based system explanations: theoretical foundations and a framework for empirical evaluation. Information Systems Research, 7, 342–362. Dirks, S., Kingston, J. K. C., & Haggith, M. (1995). Development of a KBS for personal financial planning guided by pragmatic KADS. Expert Systems with Applications, 9, 91–101. Drigs, A., Kouremenos, S., Vrettos, S., & Kouremenos, D. (2004). An expert system for job matching of the unemployed. Expert Systems with Applications, 26, 217–224. Duan, Y., Edwards, J. S., & Robins, P. C. (1998). Experiences with EXGAME: an expert system for playing a competitive business game. International Journal of Intelligent System in Accounting, Finance, and Management, 7, 1–19. Dutta, S. (1997). Strategies for implementing knowledge-based systems. IEEE Transactions on Engineering Management, 44, 79–90. Edrees, S. A., Rafea, A., Fathy, I., & Yahia, M. (2003). NEPER: a multiple strategy wheat expert system. Computers and Electronics in Agricul- ture, 40, 27–43. EI-Shal, S. M., & Morris, A. S. (2000). A fuzzy system for fault detection in statistical process control of industrial processes. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, 30, 281–292. Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–11 9 DTD 5 ARTICLE IN PRESS Fezzani, D., Piquet, H., & Foch, H. (1997). Expert system for the CAD in power electronics—application to UPS. IEEE Transaction on Power Electronics, 12, 578–586. Filis, I. V., Sabrakos, M., Yialouris, C. P., Sideridis, A. B., & Mahaman, B. (2003). GEDAS: an integrated geographical expert database system. Expert Systems with Applications, 24, 25–34. Finch, D. J., & Lees, P. F. (1997). A hybrid knowledge-based system for chemical incident management. Expert Systems with Applications, 12, 349–361. Frantti, T., & Kallio, S. (2004). Expert system for gesture recognition in terminal’s user interface. Expert Systems with Applications, 26, 189–202. Fu, Y., & Shen, R. (2004). GA based CBR approach in Q&A system. Expert Systems with Applications, 26, 167–170. Gardan, N., & Gardan, Y. (2003). An application of knowledge based modeling using scripts. Expert systems with Applications, 25, 555–568. Gilad, I., & Karni, R. (1999). Architecture of an expert system for ergonomics analysis and design. International Journal of Industrial Ergonomics, 23, 205–221. Girard, N., & Hubert, B. (1999). Modeling expert knowledge with knowledge-based systems to design decision aids. Agricultural Systems, 59, 123–144. Gjerdrum, J., Shah, N., & Papageorgiou, L. G. (2001). A combined optimization and agent-based approach to supply chain modeling and performance assessment. Production Planning and Control, 12, 81–88. Goethe, J. W., & Bronzino, J. D. (1995). An expert system for monitoring psychiatric treatment. IEEE Engineering in Medicine and Biology, November/December, 776–780. Gomolka, Z., & Orlowski, C. (2000). Knowledge management in creating hybrid systems for environmental protection. Cybernetics and Systems: An International Journal, 31, 507–529. Graham-Jones, P. J., & Mwllor, B. G. (1995). Expert and knowledge- based systems in failure analysis. Expert Systems with Applications, 2, 137–149. Gruer, P., Hilaire, V., Koukam, A., & Cetnarowicz, K. (2002). A formal framework for multi-agent systems analysis and design. Expert Systems with Applications, 23, 349–355. Guerreiro, M. A., Andrietta, S. R., & Maugeri, F. (1997). Expert system for the design of an industrial fermentation plant for the production of alcohol. Journal of Chemical Technological Biotechnology, 68, 163– 170. Hamada, K., Baba, T., Sato, K., & Yufu, M. (1995). Hybridizing a genetic algorithm with rule-based reasoning for production planning. IEEE Expert, 35, 60–67. Hatzilygeroudis, J., & Prentzas, J. (2004a). Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems. Expert Systems with Applications, 27, 63–75. Hatzilygeroudis, J., & Prentzas, J. (2004b). Using a hybrid rule-based approach in developing an intelligent tutoring system with knowledge acquisition and update capabilities. Expert Systems with Applications, 26, 477–492. Higa, K., & Lee, H. G. (1998). A graph-based approach for rule integrity and maintainability in expert system maintenance. Information and Management, 33, 273–285. Ilgun, K., Kemmerer, R. A., & Porras, P. A. (1995). State transition analysis: a rule-based intrusion detection approach. IEEE Transactions on Software Engineering, 21, 181–199. Islam, S., & Chowdhury, N. (2001). A case-based Windows graphic package for the education and training of power system restoration. IEEE Transaction on Power Systems, 16, 181–192. Jamshidi, M. A., Titli, A., Zadeh, L., & Boverie, S. (1997). Applications of fuzzy logic: Towards high machine intelligent quotient systems. Upper Saddle River, NJ: Prentice Hall. Jarmulak, J., Kerckhoffs, E. J. H., & Veen, P. P. (2001). Case-based reasoning for interpretation of data from non-destructive testing. Engineering Applications of Artificial Intelligence, 14, 401–417. Juuso, E. K. (2004). Integration of intelligent systems in development of smart adaptive systems. International Journal of Approximate reason- ing, 35, 307–337. Kawahara, K., Sasaki, H., Kubokawa, J., Asahara, H., & Sugiyama, K. (1998). A proposal of a supporting expert system for outage planning of electronic power facilities retaining high power supply reliability. IEEE Transactions on Power Systems, 13, 1453–1462. Keefe, R. M., & Preece, A. D. (1996). The development, validation and implementation of knowledge-based systems. European Journal of Operational Research, 92, 458–473. Khan, M. K., & Hafiz, N. (1999). Development of an expert system for implementation of ISO quality systems. Total Quality Management, 10, 47–59. Kim, H. S., & Im, Y. T. (1999). An expert system for cold forging process design based on a depth-first search. Journal of Materials Processing Technology, 95, 262–274. Kim, K. H., Park, J. K., Hwang, K. J., & Kim, S. H. (1995). Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Transactions on Power Systems, 10, 1534–1539. Kirkby, S. D. (1996). Integrating a GIS with an expert system to identify and manage dryland salinization. Applied Geography, 16, 289–302. Knauf, R., Gonzalez, A. J., & Abel, T. (2002). A framework for validation of rule-based systems. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 32, 281–291. Kolonder, J. L. (1994). Case-based reasoning. New York: Morgan Kaufmann. Lababidi, H., & Baker, C. G. J. (2003). Web-based expert system for food dryer selection. Computers and Chemical Engineering, 27, 997–1009. Lau, H. C., Tso, S. K., & Ho, J. K. L. (1998). Development of an intelligent task management system in a manufacturing information network. Expert Systems with Applications, 15, 165–179. Laudon, K. C., & Laudon, J. P. (2002). Essential of management information systems (5th ed). Englewood cliffs, NJ: Prentice Hall. Lee, H. J., Park, D. Y., Ahn, B. S., Park, Y. M., Park, J. K., & Venkata, S. S. (2000). A Fuzzy expert system for the integrated fault diagnosis. IEEE Transactions on Power Delivery, 15, 833–845. Lee, K. C., Han, J. H., Song, Y. U., & Lee, W. J. (1998). A fuzzy logic- driven multiple knowledge integration framework for improving the performance of expert systems. International Journal of Intelligent System in Accounting, Finance, and Management, 7, 213–222. Leon, C., Mejias, M., Luque, J., & Gonzalo, F. (1999). Expert system for the integrated management of a power utility’s communication system. IEEE Transactions on Power Delivery, 14, 1208–1212. Leung, D., & Romagnoli, J. (2000). Dynamic probabilistic model-based expert system for fault diagnosis. Computers and Chemical Engineer- ing, 24, 2473–2492. Expert Systems with Applications, 25 313–330. Leung, R. W. K., Lau, H. C. W., & Kwong, C. K. (2003). An expert system to support the optimization of ion plating process: an OLAP-based fuzzy-cum-GA approach. Li, W., Tasi, Y. P., Tasi, & Chiu, C. L. (2004). The experimental study of the expert system for diagnosing unbalance by ANN and acoustic signals. Journal of Sound and Vibration, 272, 69–83. Liao, S. H. (2001). A knowledge-based architecture for implementing military geographical intelligence system on Intranet. Expert Systems with Applications, 20, 313–324. Liao, T. W. (2003). Classification of welding flaw types with fuzzy expert systems. Expert Systems with Applications, 25, 101–111. Liau, L. C. K., Yang, C. K., & Tasi, M. T. (2004). Expert system of a crude oil distillation unit for process optimization using neural networks. Expert Systems with Applications, 26, 247–255. Lienqueo, M. E., Salgado, J. C., & Asenjo, J. A. (1999). An expert system for selection of protein purification process: experimental validation. Journal of Chemical Technology and Biotechnology, 74, 293–299. Liu, Y., & Schulz, N. N. (2002). Knowledge-based system for distribution system outage locating using comprehensive information. IEEE Transactions on Power Systems, 17, 451–456. Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–1110 DTD 5 ARTICLE IN PRESS Lopez, M. A. A., Flores, C. H., & Garcia, E. G. (2003). An intelligent tutoring system for turbine startup training of electrical power plant operators. Expert Systems with Applications, 24, 95–101. Lu, X., & Simmonds, S. H. (1997). KBES for evaluating R.C. framed buildings using fuzzy sets. Automation in Construction, 6, 121–137. Mahabir, C., Hicks, F. E., & Fayek, A. R. (2003). Application of fuzzy logic to forecast seasonal runoff. Hydrological Processes, 17, 3749–3762. Mahaman, B. D., Harizanis, P., Filis, I., Antonopoulou, E., Yialouris, C. P., & Sideridis, A. B. (2002). A diagnostic expert system for honeybee pests. Computers and Electronics in Agriculture, 36, 17–31. Mahaman, B. D., Passam, H. C., Sideridis, A. B., & Yialouris, C. P. (2003). DIARES-IPM: a diagnostic advisory rule-based expert system for integrated pest management in Solanaceous crop systems. Agricultural Systems, 76, 1119–1135. Mahmoud, M. A. A., & AL-Hammad, A. (1996). An expert system for evaluating and selection of floor finishing materials. Expert Systems with Applications, 10, 281–303. Manohar, P. A., Shivathaya, S. S., & Ferry, M. (1999). Design of an expert system for the optimization of steel compositions and process route. Expert Systems with Applications, 17, 129–134. Marchevsky, A. M., Truong, H., & Tolmachoff, T. (1997). A rule-based expert system for the automatic classification of DNA ‘ploidy’ histograms measured by the CAS 200 image analysis system. Cytometry, 30, 39–46. Martin-Baramera, M., Sancho, J. J., & Sanz, F. (2000). Controlling for change agreement in the validation of medical expert systems with no gold standard: PNEUMON-IA and PENOIR revisited. Computers and Biomedical Research, 33, 380–397. Martinez-Bejar, R., Ibanez-Cruz, F., Compton, P., & Cao, T. M. (2001). An easy-maintenance, reusable approach for building knowledge-based systems: application to landscape assessment. Expert Systems with Applications, 20, 153–162. Matsatsinis, N. F., Doumpos, M., & Zopounidis, C. (1997). Knowledge acquisition and representation for expert systems in the field of financial analysis. Expert Systems with Applications, 12, 247–262. McCoy, M. S., & Levary, R. R. (2000). A rule-based pilot performance model. International Journal of System Science, 31, 713–729. McFadden, F. R., Hoffer, J. A., & Prescott, M. B. (2000). Modern database management (5th ed). New York: Prentice-Hall. Meesad, P., & Yen, G. G. (2003). Combined numerical linguistic knowledge representation and its application to medical diagnosis. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 33, 206–222. Menzies, T. (1997). Object-oriented patterns: lessons from expert systems. Software Practice and Experience, 27, 1457–1478. Mills, K., & Gomaa, H. (2002). Knowledge-based automation of a design method for concurrent systems. IEEE Transactions on Software Engineering, 28, 228–255. Mitra, R. S., & Basu, A. (1997). Knowledge representation in MICKEY: An expert system for designing microprocessor-based systems. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 27, 467–479. Mockler, R. J., Dologite, D. G., & Gartenfeld, M. E. (2000). Talk with the experts: learning management decision-making using CAI. Cybernetics and Systems: An International Journal, 31, 431–464. Montani, S., & Bellazzi, R. (2002). Supporting decisions in medical applications: the knowledge management perspective. International Journal of Medical Informatics, 68, 79–90. Mookerjee, V. S., & Mannino, M. V. (1997). Sequential decision models for expert system optimization. IEEE Transactions on Knowledge and Data Engineering, 9, 675–687. Morrison, M. L., McCliggage, W. G., Price, G. J., Diamond, J., Sheeran, M. R. M., & Mulholland, K. M. (2002). Expert system support using a Bayesian belief network for the classification of endometrial hyperpla- sia. Journal of Pathology, 197, 403–414. Mulvaney, D., & Bristow, C. (1997). A rule-based extension to the CCC language. Software Practice and Experience, 27, 747–761. Ngai, E. W. T., & Wat, F. K. T. (2003). Design and development of a fuzzy expert system for hotel selection. Omega , 275–286. Noh, J. B., Lee, K. C., Kim, J. K., Lee, J. K., & Kim, S. H. (2000). A case- based reasoning approach to cognitive map-driven tacit knowledge management. Expert Systems with Applications, 19, 249–259. Orduna, E., Garces, F., & Handschin, E. (2003). Algorithmic-knowledge- based adaptive coordination in transmission protection. IEEE Trans- actions on Power Delivery, 18, 61–70. Ozyurt, B., & Kandel, A. (1996). A hybrid hierarchical neural network- fuzzy expert system approach to chemical process fault diagnosis. Fuzzy Sets and Systems, 83, 11–25. Padilla-Medina, J. A., & Sanchez-Marin, J. S. (2004). An adaptive fuzzy expert system to evaluate human visual performance. Fuzzy Sets and Systems, 142, 321–334. Pan, J., DeSouza, G. N., & Kak, A. C. (1998). FuzzyShell: a large-scale expert system shell using fuzzy logic for uncertainty reasoning. IEEE Transactions on Fuzzy Systems, 6, 563–581. Park, Y. M., & Lee, K. H. (1995). Application of expert system to power system restoration in local control center. Electrical Power and Energy Systems, 17, 407–415. Peng, C., Xiao, S., Nie, Z., Wang, Z., & Wang, F. (1996). Applying Bayes’ theorem in medical expert systems. IEEE Engineering in Medicine and Biology, May/June, 76–79. Plant, R. E., & Vayssieres, M. P. (2000). Combining expert system and GIS technology to implement a state-transition model of oak woodlands. Computers and Electronics in Agriculture, 27, 71–93. Punal, A., Rodriguez, J., Carrasco, E. F., Roca, E., & Lema, J. M. (2002). Expert system for the on-line diagnosis of anaerobic wastewater treatment. Water Science and Technology, 45, 195–200. Punal, A., Rodriguez, J., Franco, A., Carrasco, E. F., Roca, E., & Lema, J. M. (2001). Advanced monitoring and control of anaerobic wastewater treatment plants: diagnosis and supervision by a fuzzy-based expert system. Water Science and Technology, 43, 191–198. Qian, Y., Li, X., Jiang, Y., & Wen, Y. (2003). An expert system for real- time fault diagnosis of complex chemical processes. Expert Systems with Applications, 24, 425–432. Rahman, S., & Hazim, O. (1996). Load forecasting for multiple sites: development of an expert system-based technique. Electronic Power Systems Research, 39, 161–169. Ramaswamy, M., Sarkar, S., & Chen, Y. S. (1997). Using directed hypergraphs to verify rule-based expert systems. IEEE Transactions on Knowledge and Data Engineering, 9, 221–237. Reidsema, C., & Szczerbicki, E. (2001). A blackboard database model of the design planning process in concurrent engineering. Cybernetics and Systems: An International Journal, 32, 755–774. Reznik, L., & Dabke, K. P. (2004). Measurement models: application of intelligent methods. Measurement, 35, 47–58. Rodionov, S. N., & Martin, J. H. (1999). An expert system-based approach to prediction of year-to-year climatic variations in the north Atlantic region. International Journal of Climatology, 19, 951–974. Ruiz-Sanchez, J., Valencia-Garcia, R., Fernandez-Breis, T., Martinez-Bejar, R., & Compton, P. (2003). An approach for incremental knowledge acquisition from text. Expert Systems with Applications, 25, 77–86. Sabourin, L., & Villeneuve, F. (1996). OMEGA, an expert CAPP system. Advances in Engineering Software, 25, 51–59. Saunders, S. M., Pascoe, S., Johnson, A. P., Pilling, M. J., & Jenkin, M. E. (2003). Development and preliminary test results of expert system for the automatic generation of tropospheric VOC degradation mechan- isms. Atmospheric Environment, 37, 1723–1735. Sen, M. D. L., Minambres, J. J., Garrido, A. J., Almansa, A., & Soto, J. C. (2004). Basic theoretical results for expert systems, application to the supervision of adaptation transients in planar robots. Artificial intelligence, 152, 173–211. Sendelj, R., & Devedzic, V. (2004). Fuzzy systems based on component software. Fuzzy Sets and Systems, 141, 487–504. Shu-Hsien Liao / Expert Systems with Applications xx (2004) 1–11 11 DTD 5 ARTICLE IN PRESS Shaalan, K., EI-Badry, M., & Rafea, A. (2004a). A multiagent approach for diagnostic expert systems via the internet. Expert Systems with Applications, 27, 1–10. Shaalan, K., Rizk, M., Abdelhamid, Y., & Bahgat, R. (2004b). An expert system for the best weight distribution on ferryboats. Expert Systems with Applications, 26, 397–411. Shu, C., & Burn, D. H. (2004). Homogeneous pooling group delineation for flood frequency analysis using a fuzzy expert system with genetic enhancement. Journal of Hydrology, 291, 132–149. Soh, L. K., Tsatsoulis, C., Gineris, D., & Bertoia, C. (2004). ARKTOS: An intelligent system for SAR sea ice image classification. IEEE Transactions on Geoscience and Remote Sensing, 42, 229–248. Soliman, A., Rizzoni, G., & Kim, Y. W. (1999). Diagnosis of an automotive emission control system using fuzzy inference. Control Engineering Practice, 7, 209–216. Takahashi, M., Oono, J. I., & Saitog, K. (1995). Manufacturing process design by CBR with knowledge ware. IEEE Expert, December, 74–80. Takaoka, Y., & Mizoguchi, R. (1996). Identification of ontologies to reuse knowledge for substation fault recovery support system. Decision Support Systems, 18, 3–21. Thomson, A. J., & Willoughby, I. (2004). A web-based expert system for advising on herbicide use in Great Britain. Computers and Electronic in Agriculture, 42, 43–49. Thukaram, B. D., & Parthasarathy, K. (1997). An expert system for power system voltage stability improvement. Electrical Power and Energy Systems, 19, 385–392. Tu, S. W., Eriksson, H., Gennari, H., Shahar, Y., & Musen, M. A. (1995). Ontology-based configuration of problem-solving methods and gener- ation of knowledge-acquisition tools: application of PROTÉGÉ-II to protocol-based decision support. Artificial Intelligence in Medicine, 7, 257–289. Tucho, R., Sierra, J. M., Fernandez, J. E., Vijande, R., & Moris, G. (2003). Expert tutoring system for teaching mechanical engineering. Expert Systems with Applications, 24, 415–424. Tunez, S., Aguila, I. M., & Marin, R. (2001). An expertise model for therapy planning using abductive reasoning. Cybernetics and Systems: An International Journal, 32, 829–849. Turban, E., & Aronson, J. E. (2001). Decision support systems and intelligent systems, sixth Edition (6th ed). Hong Kong: Prentice International Hall. Valenzuela, L. M., Bentley, J. M., & Lorenz, R. D. (2004). Expert system for integrated control and supervision of dry-end sections of paper machines. IEEE Transactions on Industry Applications, 40, 680–691. Vegh, J. (2003). The ‘carbon aontamination’ rule set implemented in an ‘embedded expert system’. Journal of Electron Spectroscopy and Related Phenomena, 133, 87–101. Vlahavas, I., Stamelos, I., Refanidis, I., & Tsoukias, A. (1999). ESSE: an expert system for software evaluation. Knowledge-Based Systems, 12, 183–197. Volberda, H. W., & Rutges, A. (1999). FARSYS: a knowledge-based system for managing strategic change. Decision Support Systems, 26, 99–123. Vranes, S., & Stanojevic, M. (1995). Integrated multiple paradigms within the blackboard framework. IEEE Transactions on Software Engineer- ing, 21, 244–262. Vranes, S., & Stanojevic, M. (1999). Design knowledge representation in Prolog/Rex. Engineering Applications of Artificial Intelligence, 12, 221–228. Wang, X., Qu, H., Liu, P., & Cheng, Y. (2004). A self-learning expert system for diagnosis in traditional Chinese medicine. Expert Systems with Applications, 26, 557–566. Wasiewicz, P., Janczak, T., Mulawka, J. J., & Plucienniczak, A. (2000). The inference based on molecular computing. Cybernetics and Systems: An International Journal, 31, 283–315. Wei, M. S., & Weber, F. (1996). An expert system for waste management. Journal of Environmental Management, 46, 345–358. Wiig, K. M. (1994). Knowledge management, the central management focus for intelligent-acting organization. Arlington: Schema Press. Wong, S. V., & Hamouda, A. M. S. (2002). A fuzzy logic based expert system for machinability data-on-demand on the Internet. Journal of Materials Processing Technology, 124, 57–66. Wong, S. V., & Hamouda, A. M. S. (2003). The development of an online knowledge-based expert system for machinability data selection. Knowledge-based Systems, 16, 215–229. Wu, T. P., & Chen, S. M. (1999). A new method for constructing membership functions and fuzzy rules from training examples. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 29, 25–37. Wu, C. H., & Lee, S. J. (1997). Enhanced high-level Petri nets with multiple colors for knowledge verification/validation of rule-based expert systems. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 27, 760–773. Xirogiannis, G., Stefanou, J., & Glykas, M. (2004). A fuzzy cognitive map approach to support urban design. Expert Systems with Applications, 26, 257–268. Yan, H., Jiang, Y., Zheng, J., Fu, B., Xiao, S., & Peng, C. (2004). The internet-based knowledge acquisition and management method to construct large-scale distributed medical expert system. Computer Methods and Programs in Biomedicine, 74, 1–10. Yang, B. S., Han, T., & Kim, Y. S. (2004). Integration of ART-Kohoene neural network and case-based reasoning for intelligent fault diagnosis Yu, R., Iung, B., & Panetto, H. (2003). A multi-agents based E-maintenance system with case-based reasoning decision support. Engineering Applications of artificial Intelligence, 16, 321–333. Expert Systems with Applications, 26, 387–395. Zha, X., & Lim, S. Y. E. (2000). Assembly/disassembly task planning and simulation using expert Petri nets. International Journal of Production Research, 15, 3639–3676. Zhou, Q., Huang, G. H., & Chan, C. W. (2004). Development of an intelligent decision support system for air pollution control at coal-fired power plants. Expert Systems with Applications, 26, 335–356. Zhu, A. X., Band, L. E., Dutton, B., & Nimlos, T. J. (1996). Automated soil inference under fuzzy logic. Ecological Modeling, 90, 123–145. Zupan, B., & Cheng, M. K. (1998). Optimization of rule-based systems using state space graphs. IEEE Transactions on Knowledge and Data Engineering, 10, 238–254. Expert system methodologies and applications-a decade review from 1995 to 2004 Introduction Rule-based systems and their applications Knowledge-based systems and their applications Neural networks and their applications Fuzzy expert systems and their applications Object-oriented methodology and their applications Case-based reasoning and their applications Modeling and their applications System architecture and their applications Intelligent agents and their applications Ontology and their applications Database methodology and their applications Discussions, limitations, and suggestions Discussions Limitations Suggestions Conclusions References